In this test, we split the training and testing sequences similar to the tests conducted in the original paper of EL. [1]}. To show the results,
we compared the root-mean-squared errors (RMSE) between the ground-truth IMU positions and the estimated ones by different algorithms.
The results are shown in Table REF . Those computed positional RMSE values clearly demonstrate that the proposed method outperforms competing ones by wide margins.
Compared to traditional method and VINS [2]}, our method is able to better represent the IMU model. Compared to the DNN methods, our method regresses learn-able terms which are of better accuracy and make probabilistic sensor fusion possible.
